class_docstr: AdaGrad optimizer.
  
  This class implements the AdaGrad optimizer described in *Adaptive Subgradient
  Methods for Online Learning and Stochastic Optimization*, and available at
  http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
  
  This optimizer updates each weight by::
  
      grad = clip(grad * rescale_grad, clip_gradient) + wd * weight
      history += square(grad)
      weight -= learning_rate * grad / (sqrt(history) + epsilon)
  
  This optimizer accepts the following parameters in addition to those accepted
  by :class:`.Optimizer`.
  
  See Also
  ----------
  :meth:`mxnet.ndarray.sparse.adagrad_update`.
  
  Parameters
  ----------
  learning_rate : float, default 0.01
      The initial learning rate. If None, the optimization will use the
      learning rate from ``lr_scheduler``. If not None, it will overwrite
      the learning rate in ``lr_scheduler``. If None and ``lr_scheduler``
      is also None, then it will be set to 0.01 by default.
  epsilon : float, default 1e-6
      Small value to avoid division by 0.
  use_fused_step : bool, default True
      Whether or not to use fused kernels for optimizer.
      When use_fused_step=False or grad is not sparse, step is called,
      otherwise, fused_step is called.
code: def __init__(
      self,
      learning_rate=0.01,
      epsilon=1e-6,
      use_fused_step=True,
      **kwargs,
  ):
      if kwargs.get("eps") is not None:
          raise DeprecationWarning(
              "parameter 'eps' is deprecated. Please use 'epsilon' instead..."
          )
      super(AdaGrad, self).__init__(
          learning_rate=learning_rate,
          use_fused_step=use_fused_step,
          **kwargs,
      )
      self.epsilon = epsilon
  
file_path: mxnet/python/mxnet/optimizer/adagrad.py
imports: from __future__ import absolute_import
  from ..ndarray import zeros, clip, sqrt, square
  from ..ndarray import sparse
  from .optimizer import Optimizer, register
label_desc: Use image node_id 1 to create a new AdaGrad object from inherited base classes: Optimizer with example: obj = AdaGrad(learning_rate, epsilon, use_fused_step)